This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.

Conditional probabilities

Setting up problems

Bayes' theorem applied

Central limit theorem

Samples from a rolled die

Simulating central limit theorem

Probability distributions

Bernoulli distribution

Binomial distribution

Normal distribution

Probability and Sampling Distributions

In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables.  The exercises will prepare you for an analytical assessment or stats-based coding question.

Statistiche descrittive

Media o mediana

Deviazione standard a mano

Dati categorici

Tecniche di codifica

Esplorare i prezzi dei laptop

Due o più variabili

Tipi di relazioni

Correlazione di Pearson

Sensibilità agli outlier

Exploratory Data Analysis

Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Confidence intervals

Confidence interval by hand

Applying confidence intervals

Hypothesis testing

One tailed z-test

Two tailed t-test

Power and sample size

Effect on type II error

Calculating sample size

Visualizing the relationship

Multiple testing

Calculating error rates

Bonferroni correction

Statistical Experiments and Significance Testing

Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff. 

Regression models

Linear regression

Logistic regression

Evaluating models

Regression evaluation

Classification evaluation

Missing data and outliers

Handling null values

Identifying outliers

Bias-variance tradeoff

Test and training error

Visualizing the tradeoff

Wrapping up

Regression and Classification

Vuoi ottenere il prossimo lavoro o affinare le tue abilità per i colloqui di statistica per restare al top? Preparati a padroneggiare i classici concetti da colloquio, dalle probabilità condizionate all’A/B testing fino al compromesso bias-varianza, e molto altro! Lavorerai con una raccolta eterogenea di insiemi di dati, tra cui risultati di esperimenti sul web e dati meteo australiani. Al termine del corso, potrai affrontare con sicurezza il tuo prossimo colloquio e rispondere a qualsiasi domanda di statistica con l’aiuto di Python!

Hypothesis Testing in Python

Supervised Learning with scikit-learn

Prepara il tuo colloquio di statistica ripassando probabilità condizionali, test A/B, bias-varianza.

Esercitarsi con le domande di statistica per i colloqui in Python

Preparati per il tuo prossimo colloquio di statistica ripassando concetti come probabilità condizionate, test A/B, compromesso bias-varianza e altro ancora. 

Correlazione di Pearson

Esercitarsi con le domande di statistica per i colloqui in Python

esercizio interattivo pratico